Scaling Performance Parameters of Reciprocating Engines for Sustainable Energy System Optimization Modelling

The increased share of variable renewable energy sources such as wind and solar power poses constraints on the stability of the grid and the security of supply due to the imbalance between electricity production and demand. Chemical storage or power-to-X technologies can provide the flexibility that is needed to overcome this issue. To quantify the needs of such storage systems, energy system optimization models (ESOMs) are used, guiding policy makers in nationwide energy planning. The key input parameters for such models are the capacity and efficiency values of the conversion devices. Gas turbines, reciprocating engines, fuel cells and Rankine engines are often mentioned here as cogeneration technologies. Their performance parameters will however need to be revised when switching from fossil to renewable fuels. This study therefore investigates the possibility of using size-based scaling laws to predict the efficiency and power values of one type of conversion technology: the reciprocating engine. The most straightforward scaling laws are the ones based on the fundamental engine performance parameters and are constructed by fitting an arithmetic function for a large set of representative engine data. Their accuracy was tested with a case study, consisting of thirty large-bore, spark-ignited gas engines. Two alternative methods were also investigated: scaling laws based on the Willans line method and scaling laws based on the similarity theory. Their use is deemed impractical for the current research problem.